Abstract Relapse remains the leading cause of treatment failure following hematopoietic cell transplantation (HCT) for hematologic malignancies. Circulating tumor DNA (ctDNA) has emerged as a promising minimally invasive biomarker for measurable residual disease (MRD) assessment and early relapse detection; however, the prognostic significance of ctDNA in the post-transplant setting has not been comprehensively synthesized. We conducted a systematic review and meta-analysis in accordance with PRISMA guidelines and registered the protocol in PROSPERO (CRD420261392100). PubMed, Embase, Web of Science, EBSCO, Cochrane CENTRAL, and supplementary sources were searched through November 2025. Eligible studies evaluated tumor-specific ctDNA or tumor-informed/tumor-associated cfDNA in patients undergoing allogeneic or autologous HCT for hematologic malignancies. Random-effects meta-analyses were performed for relapse/progression, overall survival (OS), and relapse-free/progression-free survival (RFS/PFS). Studies evaluating total cfDNA quantity, methylation-based cfDNA profiling, cfRNA, or chimerism-only monitoring were synthesized narratively. Ten observational cohort studies comprising 883 patients met inclusion criteria. Across acute leukemias, lymphomas, multiple myeloma, and myelodysplastic syndromes, ctDNA/cfDNA positivity was consistently associated with adverse outcomes. The pooled hazard ratio (HR) for relapse or disease progression was 12.57 (95% CI: 4.59–34.46; p 50% was considered indicative of substantial heterogeneity. Because relatively few studies contributed to several pooled analyses, pooled estimates and heterogeneity statistics were interpreted cautiously. Pre-specified subgroup analyses according to disease subtype, HCT modality, timing of ctDNA assessment, and assay platform were planned. However, formal subgroup analyses were limited by the small number of eligible studies contributing to each pooled endpoint. Sensitivity analyses were planned by sequential exclusion of studies judged to have high overall risk of bias. Publication bias was evaluated visually using funnel plots and statistically using Egger’s regression test when sufficient studies were available for pooled analysis. Formal publication bias assessment was limited by the small number of studies contributing to several pooled outcomes. 3. Results 3.1. Characteristics of Included Studies The 10 included studies encompassed a total of 883 patients, with sample sizes ranging from 10 to 177 participants per study. Studies were conducted internationally, with most originating from the United States (n = 5) [ 20, 21, 22, 23, 24]. Additional studies were conducted in Japan [ 25], Denmark [ 26], Turkey [ 27], South Korea [ 28], and Germany [ 29]. The spectrum of underlying hematologic malignancies was broad and included AML, ALL, MDS, MM, and various malignant lymphomas. Overall, six studies employed retrospective observational cohort designs [ 20, 21, 22, 25, 26, 27], while four were prospective cohort or pilot studies [ 23, 24, 28, 29]. Eight studies focused on allo-HCT [ 21, 22, 23, 24, 25, 26, 27, 29], whereas two evaluated autologous HCT populations [ 20, 28]. The methodologies used for circulating nucleic acid assessment were heterogeneous and included targeted next-generation sequencing (NGS), droplet digital PCR (ddPCR), WGBS, spectrophotometric total cfDNA quantification, and real-time quantitative PCR (RQ-PCR) chimerism assays. Because these approaches capture biologically distinct processes, studies were categorized into: (1) tumor-specific ctDNA studies, (2) tumor-associated cfDNA/cfDNA-MRD studies, and (3) non-tumor-specific approaches, including total cfDNA quantification, methylation-based cfDNA profiling, and chimerism-based monitoring. The main characteristics of included studies are summarized in Table 1. 3.2. Survival Outcomes Eight studies evaluated associations between circulating nucleic acid biomarkers and OS, RFS, or PFS. Because included studies assessed biologically distinct biomarker approaches, pooled quantitative analyses were restricted to tumor-specific ctDNA/cfDNA studies with extractable hazard ratios, whereas total cfDNA, methylation-based profiling, and chimerism studies were interpreted qualitatively. Among tumor-associated cfDNA studies, Pasca et al. (2023) demonstrated that tumor-informed cfDNA-MRD positivity at day +90 after allo-HCT in myeloid malignancies was strongly associated with inferior RFS and OS, with a 2-year cumulative incidence of relapse of 76% among cfDNA-positive patients compared with 21% among negative patients ( p < 0.001) [ 24]. Patel et al. (2025) likewise reported that tumor-associated cfDNA positivity was associated with increased mortality and relapse risk following allogeneic HCT in AML (HR for OS = 5.4; 95% CI: 2.5–11.8; p < 0.0001) [ 14]. Waterhouse et al. (2022) demonstrated that longitudinal patient-specific cfDNA monitoring enabled early relapse detection, including extramedullary relapse, and was associated with inferior survival outcomes [ 29]. In contrast, Yegin et al. (2020), who evaluated total cfDNA quantity rather than tumor-specific ctDNA, did not identify a statistically significant association between cfDNA levels and OS [ 27]. Across studies eligible for quantitative synthesis, pooled meta-analysis demonstrated that ctDNA/cfDNA positivity was significantly associated with inferior OS (pooled HR = 7.45; 95% CI: 4.11–13.48) and inferior RFS/PFS (pooled HR = 4.46; 95% CI: 2.22–8.97). Although statistical heterogeneity was low, interpretation was limited by the relatively small number of studies contributing to each pooled endpoint. A summary of survival outcome data is presented in Table 2. 3.3. Prediction of Graft-Versus-Host Disease and Other Complications Beyond relapse surveillance, emerging evidence suggests that broader cell-free nucleic acid profiling approaches may also provide clinically relevant information regarding non-relapse complications following HCT. Cheng et al. (2022) utilized low-coverage whole-genome bisulfite sequencing of plasma cfDNA to simultaneously evaluate multiple post-transplant complications, including GVHD, infection, relapse, and graft failure [ 23]. Their findings demonstrated that methylation-based cfDNA profiling could capture tissue injury and immune-related signals associated with post-HCT complications, supporting the potential role of broader liquid biopsy approaches beyond tumor-specific relapse monitoring. 3.4. Engraftment and Transplant-Related Mortality Several studies additionally explored associations between circulating nucleic acid biomarkers and transplant-related complications, including TRM, NRM, and graft-related outcomes. Yegin et al. (2020) reported an inverse relationship between pre-transplant total cfDNA levels and transplant-related complications, with lower total cfDNA levels associated with increased complications and mortality [ 27]. Because this study measured total cfDNA quantity by spectrophotometry rather than tumor-specific ctDNA, the biological interpretation of these findings likely differs from mutation-informed MRD approaches. 3.5. Relapse Prediction All included studies evaluated relapse-related outcomes, although substantial heterogeneity existed in biomarker methodology and study design ( Table 4). To minimize biological heterogeneity, quantitative pooling was restricted to tumor-specific ctDNA/cfDNA studies with extractable hazard ratios, while total cfDNA, methylation-based profiling, and chimerism studies were interpreted qualitatively. Across studies eligible for quantitative synthesis, the pooled HR for relapse or disease progression associated with ctDNA/cfDNA positivity was 12.57 (95% CI: 4.59–34.46), supporting a strong association between molecular positivity and adverse post-transplant oncologic outcomes. 3.6. Risk of Bias Assessment Risk of bias assessment using the QUIPS tool demonstrated an overall moderate risk of bias across included studies. Most studies showed low risk of bias in outcome measurement and statistical reporting domains, reflecting the use of objective clinical endpoints and generally transparent statistical analyses. The most frequent sources of potential bias involved the prognostic factor measurement and study confounding domains. Variability in ctDNA assay methodologies, timing of sample collection, ctDNA thresholds, and plasma processing protocols contributed to moderate methodological heterogeneity across studies. In addition, several retrospective studies lacked comprehensive multivariable adjustment for established prognostic factors, including disease risk, cytogenetic abnormalities, conditioning intensity, donor type, or GVHD status. Studies evaluating total cfDNA quantity or non-tumor-specific approaches were generally judged to have a higher risk of bias due to limitations in biological specificity and assay standardization. Retrospective studies additionally demonstrated increased susceptibility to attrition and participation bias compared with prospective cohort studies. A detailed summary of the QUIPS assessment is presented in Table 5. Risk of Bias Domains (QUIPS): Study Participation; Study Attrition; Prognostic Factor Measurement; Outcome Measurement; Study Confounding; Statistical Analysis and Reporting. Interpretation: Most studies demonstrated low risk of bias in outcome measurement and statistical reporting domains. Moderate or high-risk judgments were primarily driven by variability in ctDNA assay methodologies, inconsistent sampling schedules, retrospective study design, incomplete adjustment for established prognostic variables, and limited sample sizes. 3.7. Meta-Analysis of ctDNA Positivity and Survival Outcomes Only studies evaluating tumor-specific ctDNA or tumor-informed/tumor-associated cfDNA with extractable hazard ratios were included in the quantitative meta-analysis. Studies evaluating total cfDNA quantity, methylation-based cfDNA profiling, cfRNA, or chimerism-only monitoring were excluded from pooled analyses because of substantial biological and methodological heterogeneity. The summarized HR data used in the pooled analyses are presented in Table 6. Meta-analysis demonstrated statistically significant associations between ctDNA/cfDNA positivity and adverse clinical outcomes across all primary endpoints. Although statistical heterogeneity across pooled analyses was low, interpretation should remain cautious because only a limited number of studies contributed to each endpoint, thereby reducing the power of heterogeneity statistics to detect true between-study variability. 4. Discussion This systematic review and meta-analysis demonstrate that tumor-specific ctDNA positivity is consistently associated with increased relapse risk and inferior survival outcomes following HCT across multiple hematologic malignancies. Quantitative synthesis revealed statistically significant associations between ctDNA/cfDNA positivity and relapse or disease progression, OS, and RFS/PFS. Collectively, these findings support the potential role of ctDNA-based MRD monitoring as a promising non-invasive biomarker for post-transplant molecular surveillance and risk stratification. Importantly, substantial biological and methodological heterogeneity exists among circulating nucleic acid biomarkers evaluated in the transplant literature. Tumor-specific ctDNA assays, tumor-informed cfDNA-MRD approaches, total cfDNA quantification, methylation-based cfDNA profiling, cfRNA analysis, and chimerism-based monitoring are biologically distinct methodologies that should not be interpreted interchangeably. Tumor-specific ctDNA primarily reflects residual malignant clones and disease kinetics, whereas total cfDNA levels may additionally reflect tissue injury, inflammation, endothelial damage, infection, or conditioning-related toxicity. Likewise, methylation-based cfDNA and cfRNA approaches may capture immune activation, GVHD, or organ injury rather than direct molecular relapse. To address this heterogeneity, the present meta-analysis was restricted to tumor-specific ctDNA and tumor-informed/tumor-associated cfDNA studies with extractable hazard ratios, whereas non-tumor-specific approaches were synthesized narratively. Nevertheless, conceptual overlap among these biomarker categories remains an important limitation when interpreting the broader liquid biopsy literature in HCT. Although statistical heterogeneity across pooled analyses was low, these findings should be interpreted cautiously. Only a limited number of studies contributed to each pooled endpoint, thereby reducing the statistical power of heterogeneity metrics such as the I 2 statistic to detect true between-study variability. Moreover, substantial clinical heterogeneity remained across studies, including differences in underlying malignancies, transplant modalities, assay platforms, ctDNA targets, sampling schedules, and definitions of molecular positivity. Accordingly, the apparent consistency of pooled estimates does not necessarily imply methodological uniformity, and the magnitude of pooled hazard ratios should be interpreted within the context of relatively small observational datasets. Another major challenge limiting broader clinical implementation is the absence of assay standardization. Included studies used heterogeneous technologies, including targeted NGS, ddPCR, patient-informed assays, multiplex PCR-NGS, methylation-based profiling, and total cfDNA quantification. Considerable variability also existed in plasma processing protocols, sequencing depth, ctDNA thresholds, timing of sample collection, and definitions of ctDNA positivity. These differences complicate cross-study comparisons and currently preclude the establishment of universally accepted thresholds for clinical decision-making. International collaborative efforts aimed at harmonizing ctDNA collection, processing, analysis, and reporting standards will therefore be essential for future clinical translation. Several limitations of this review should be acknowledged. First, only a limited number of studies contributed to each pooled endpoint, reducing the precision of pooled estimates and limiting the reliability of heterogeneity assessments. Second, most included studies were observational cohorts conducted at single institutions, increasing susceptibility to selection bias and residual confounding. Third, substantial methodological variability existed in assay platforms, timing of sampling, ctDNA targets, and endpoint definitions. Fourth, despite separating tumor-specific ctDNA studies from total cfDNA, methylation-based profiling, and chimerism-based approaches in the revised analyses, conceptual overlap among circulating nucleic acid biomarkers remains a source of complexity within the literature. Finally, although the review protocol was prospectively registered, publication bias could not be robustly assessed because relatively few studies contributed to several pooled analyses. Despite these limitations, this study provides one of the first focused quantitative syntheses specifically evaluating tumor-specific ctDNA monitoring following HCT across multiple hematologic malignancies. The consistent associations observed between ctDNA positivity and adverse oncologic outcomes support continued investigation of ctDNA as a clinically relevant prognostic biomarker in the transplant setting. Future priorities should include multicenter prospective validation studies, assay harmonization initiatives, standardized sampling strategies, and randomized ctDNA-guided interventional trials to determine whether earlier molecular relapse detection can ultimately improve patient outcomes after HCT. 5. Conclusions In conclusion, tumor-specific ctDNA positivity is consistently associated with increased relapse risk and inferior survival outcomes following hematopoietic cell transplantation. These findings support the potential role of ctDNA-based MRD monitoring as a promising non-invasive biomarker for post-transplant risk stratification and molecular surveillance. However, further assay standardization, prospective multicenter validation, and ctDNA-guided interventional trials are required before routine clinical implementation can be recommended. Author Contributions D.T.D.: data curation, analysis, and writing the original draft. H.T. and A.T.: data curation and analysis. J.L.E.: Conceptualization, analysis, writing—review and editing. All authors have read and agreed to the published version of the manuscript. Funding This research received no external funding. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement All data generated or analyzed during this study are included in this published article. Conflicts of Interest The authors declare no conflicts of interest. Abbreviations The following abbreviations are used in this manuscript: Disease and Clinical Terms ALL Acute lymphoblastic leukemia AML Acute myeloid leukemia CLL Chronic lymphocytic leukemia CMML Chronic myelomonocytic leukemia DLBCL Diffuse large B-cell lymphoma HL Hodgkin lymphoma MDS Myelodysplastic syndromes MM Multiple myeloma MPN Myeloproliferative neoplasm NHL non-Hodgkin lymphoma Transplant-Related Terms Allo-HCT Allogeneic hematopoietic cell transplantation GVHD Graft-versus-host disease GVL Graft-versus-leukemia HCT Hematopoietic cell transplantation NRM non-relapse mortality TRM Transplant-related mortality Molecular and Laboratory Terms cfDNA Cell-free DNA cfRNA Cell-free RNA ctDNA Circulating tumor DNA ddPCR Droplet digital polymerase chain reaction MFC Multiparameter flow cytometry MRD Minimal residual disease NGS Next-generation sequencing PCR Polymerase chain reaction RQ-PCR Real-time quantitative polymerase chain reaction TA-cfDNA Tumor-associated cell-free DNA WGBS Whole-genome bisulfite sequencing Statistical and Methodological Terms AUC Area under the curve CI Confidence interval CIR Cumulative incidence of relapse HR Hazard ratio NR Not reported OS Overall survival PFS Progression-free survival PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses QUIPS Quality in Prognosis Studies RFS Relapse-free survival Other APC Article processing charges CAR-T Chimeric antigen receptor T-cell References Greco, R.; Ruggeri, A.; McLornan, D.P.; Snowden, J.A.; Alexander, T.; Angelucci, E.; Averbuch, D.; Bazarbachi, A.; Hazenberg, M.D.; Kalwak, K.; et al. Indications for haematopoietic cell transplantation and CAR-T for haematological diseases, solid tumours and immune disorders: 2025 EBMT practice recommendations. Bone Marrow Transplant. 2025, 60, 1499–1525. [] [ CrossRef] Rieger, M.J.; Stolz, S.M.; Müller, A.M.; Schwotzer, R.; Nair, G.; Schneidawind, D.; Manz, M.G.; Schanz, U. Haploidentical transplant with posttransplant cyclophosphamide vs matched related and unrelated donor transplant in acute myeloid leukemia and myelodysplastic neoplasm. Bone Marrow Transplant. 2023, 58, 1121–1129. [] [ CrossRef] Akhmedov, M.; Espinoza, J.L. Addressing the surge of infections by multidrug-resistant Enterobacterales in hematopoietic cell transplantation. Blood Rev. 2024, 68, 101229. [] [ CrossRef] Sauerer, T.; Velázquez, G.F.; Schmid, C. Relapse of acute myeloid leukemia after allogeneic stem cell transplantation: Immune escape mechanisms and current implications for therapy. Mol. Cancer 2023, 22, 180. [] [ CrossRef] [ PubMed] Heuser, M.; Freeman, S.D.; Ossenkoppele, G.J.; Buccisano, F.; Hourigan, C.S.; Ngai, L.L.; Tettero, J.M.; Bachas, C.; Baer, C.; Béné, M.-C.; et al. 2021 Update on MRD in acute myeloid leukemia: A consensus document from the European LeukemiaNet MRD Working Party. Blood 2021, 138, 2753–2767. [] [ CrossRef] [ PubMed] Sorror, M.L. The use of prognostic models in allogeneic transplants: A perspective guide for clinicians and investigators. Blood 2023, 141, 2173–2186. [] [ CrossRef] [ PubMed] Wan, J.C.M.; Massie, C.; Garcia-Corbacho, J.; Mouliere, F.; Brenton, J.D.; Caldas, C.; Pacey, S.; Baird, R.; Rosenfeld, N. Liquid biopsies come of age: Towards implementation of circulating tumour DNA. Nat. Rev. Cancer 2017, 17, 223–238. [] [ CrossRef] Martínez-Castedo, B.; Camblor, D.; Martín-Arana, J.; Carbonell-Asins, J.; García-Micó, B.; Gambardella, V.; Huerta, M.; Roselló, S.; Roda, D.; Gimeno-Valiente, F.; et al. Minimal residual disease in colorectal cancer. Tumor-informed versus tumor-agnostic approaches: Unraveling the optimal strategy. Ann. Oncol. 2025, 36, 263–276. [] [ CrossRef] Sorbini, M.; Carradori, T.; Togliatto, G.M.; Vaisitti, T.; Deaglio, S. Technical Advances in Circulating Cell-Free DNA Detection and Analysis for Personalized Medicine in Patients’ Care. Biomolecules 2024, 14, 498. [] [ CrossRef] Kurtz, D.M.; Esfahani, M.S.; Scherer, F.; Soo, J.; Jin, M.C.; Liu, C.L.; Newman, A.M.; Dührsen, U.; Hüttmann, A.; Casasnovas, O.; et al. Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction. Cell 2019, 178, 699–713.e19. [] [ CrossRef] Martello, M.; Solli, V.; Mazzocchetti, G.; Solimando, A.G.; Bezzi, D.; Taurisano, B.; Kanapari, A.; Poletti, A.; Borsi, E.; Armuzzi, S.; et al. High level of circulating cell-free tumor DNA at diagnosis correlates with disease spreading and defines multiple myeloma patients with poor prognosis. Blood Cancer J. 2024, 14, 208. [] [ CrossRef] Zou, H.; Liu, W.; Wang, X.; Wang, Y.; Wang, C.; Qiu, C.; Liu, H.; Shan, D.; Xie, T.; Huang, W.; et al. Dynamic monitoring of circulating tumor DNA reveals outcomes and genomic alterations in patients with relapsed or refractory large B-cell lymphoma undergoing CAR T-cell therapy. J. Immunother. Cancer 2024, 12, e008450. [] [ CrossRef] Frank, M.J.; Hossain, N.M.; Bukhari, A.; Dean, E.; Spiegel, J.Y.; Claire, G.K.; Kirsch, I.; Jacob, A.P.; Mullins, C.D.; Lee, L.W.; et al. Monitoring of Circulating Tumor DNA Improves Early Relapse Detection After Axicabtagene Ciloleucel Infusion in Large B-Cell Lymphoma: Results of a Prospective Multi-Institutional Trial. J. Clin. Oncol. 2021, 39, 3034–3043. [] [ CrossRef] [ PubMed] Zhou, X.; Lang, W.; Mei, C.; Ren, Y.; Ma, L.; Jiang, L.; Ye, L.; Xu, G.; Luo, Y.; Liu, L.; et al. Serial monitoring of circulating tumour DNA on clinical outcome in myelodysplastic syndromes and acute myeloid leukaemia. Clin. Transl. Med. 2023, 13, e1349. [] [ CrossRef] Shastri, T.; Trabolsi, A.; Arumov, A.; Schatz, J.H. Bispecific Antibodies in Hematologic Malignancies: Attacking the Frontline. BioDrugs 2025, 39, 793–814. [] [ CrossRef] Tan, X.; Yan, H.; Chen, L.; Zhang, Y.; Sun, C. Clinical Value of ctDNA in Hematological Malignancies (Lymphomas, Multiple Myeloma, Myelodysplastic Syndrome, and Leukemia): A Meta-Analysis. Front. Oncol. 2021, 11, 632910. [] [ CrossRef] Lee, J.S.; Cho, E.H.; Kim, B.; Hong, J.; Kim, Y.-G.; Kim, Y.; Jang, J.-H.; Lee, S.-T.; Kong, S.-Y.; Lee, W.; et al. Clinical Practice Guideline for Blood-based Circulating Tumor DNA Assays. Ann. Lab. Med. 2024, 44, 195–209. [] [ CrossRef] [ PubMed] Tierney, J.F.; Stewart, L.A.; Ghersi, D.; Burdett, S.; Sydes, M.R. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials 2007, 8, 16. [] [ CrossRef] Hayden, J.A.; Van Der Windt, D.A.; Cartwright, J.L.; Côté, P.; Bombardier, C. Assessing bias in studies of prognostic factors. Ann. Intern. Med. 2013, 158, 280–286. [] [ CrossRef] Dhakal, B.; Sharma, S.; Balcioglu, M.; Shchegrova, S.; Malhotra, M.; Zimmermann, B.; Billings, P.R.; Harrington, A.; Sethi, H.; Aleshin, A.; et al. Assessment of Molecular Residual Disease Using Circulating Tumor DNA to Identify Multiple Myeloma Patients at High Risk of Relapse. Front. Oncol. 2022, 12, 786451. [] [ CrossRef] [ PubMed] Patel, V.; Kabat, M.; Ip, A.; Kaur, S.; Suh, H.C.; Cho, C.; Vesole, D.; Donato, M.; Albitar, M.; Rowley, S.D. Peripheral Blood Tumor Associated Cell-Free DNA Testing as a Predictor for Relapse Postallogeneic Stem Cell Transplant for Acute Myelogenous Leukemia. Transpl. Cell Ther. 2025, 31, 1010–1019. [] [ CrossRef] Herrera, A.F.; Kim, H.T.; Kong, K.A.; Faham, M.; Sun, H.; Sohani, A.R.; Alyea, E.P.; Carlton, V.E.; Chen, Y.; Cutler, C.S.; et al. Next-generation sequencing-based detection of circulating tumour DNA After allogeneic stem cell transplantation for lymphoma. Br. J. Haematol. 2016, 175, 841–850. [] [ CrossRef] Cheng, A.P.; Cheng, M.P.; Loy, C.J.; Lenz, J.S.; Chen, K.; Smalling, S.; Burnham, P.; Timblin, K.M.; Orejas, J.L.; Silverman, E.; et al. Cell-free DNA profiling informs all major complications of hematopoietic cell transplantation. Proc. Natl. Acad. Sci. USA 2022, 119, e2113476118. [] [ CrossRef] Pasca, S.; Guo, M.Z.; Wang, S.; Stokvis, K.; Shedeck, A.; Pallavajjala, A.; Shams, C.; Pallavajjala, R.; DeZern, A.E.; Varadhan, R.; et al. Cell-free DNA measurable residual disease as a predictor of postallogeneic hematopoietic cell transplant outcomes. Blood Adv. 2023, 7, 4660–4670. [] [ CrossRef] Nakamura, S.; Yokoyama, K.; Shimizu, E.; Yusa, N.; Kondoh, K.; Ogawa, M.; Takei, T.; Kobayashi, A.; Ito, M.; Isobe, M.; et al. Prognostic impact of circulating tumor DNA status post-allogeneic hematopoietic stem cell transplantation in AML and MDS. Blood 2019, 133, 2682–2695. [] [ CrossRef] Haugaard, A.K.; Madsen, H.O.; Marquart, H.V.; Rosthøj, S.; Masmas, T.N.; Heilmann, C.; Müller, K.G.; Ifversen, M. Highly sensitive chimerism detection in blood is associated with increased risk of relapse after allogeneic hematopoietic cell transplantation in childhood leukemia. Pediatr. Transpl. 2019, 23, e13549. [] [ CrossRef] Yegin, Z.A.; Can, F.; Gökçen, S.; Sadioğlu, R.E.; Özkurt, Z.N.; İlHan, Ç.; Yağcı, M. The Impact of Pre-transplant Cell-free DNA Levels on Leukemia Relapse and Transplant-related Complications in Allogeneic Hematopoietic Stem Cell Transplant Recipients. Balk. Med. J. 2020, 37, 138–143. [] [ CrossRef] [ PubMed] Kim, J.; Le, T.M.; Lee, D.; Nguyen, H.D.T.; Cho, H.J.; Sohn, S.K.; Kim, J.G.; Jeong, S.-Y.; Ham, J.Y.; Jeong, J.Y.; et al. Circulating-tumor DNA Assessment in Diffuse Large B-cell Lymphoma to Determine Up-front Stem Cell Transplantation: A Pilot Study. In Vivo 2024, 38, 372–379. [] [ CrossRef] [ PubMed] Waterhouse, M.; Pennisi, S.; Pfeifer, D.; Scherer, F.; Zeiser, R.; Duyster, J.; Bertz, H.; Finke, J.; Duque-Afonso, J. Monitoring of Measurable Residual Disease Using Circulating DNA after Allogeneic Hematopoietic Cell Transplantation. Cancers 2022, 14, 3307. [] [ CrossRef] [ PubMed] Abdulmawjood, B.; Roma-Rodrigues, C.; Fernandes, A.R.; Baptista, P.V. Liquid biopsies in myeloid malignancies. Cancer Drug Resist. 2019, 2, 1044–1061. [] [ CrossRef] Kolb, H.-J. Graft-versus-leukemia effects of transplantation and donor lymphocytes. Blood 2008, 112, 4371–4383. [] [ CrossRef] [ PubMed] Liang, E.C.; Dekker, S.E.; Sabile, J.M.G.; Torelli, S.; Zhang, A.; Miller, K.; Shiraz, P.; Hayes-Lattin, B.; Leonard, J.T.; Muffly, L. Next-generation sequencing–based MRD in adults with ALL undergoing hematopoietic cell transplantation. Blood Adv. 2023, 7, 3395–3402. [] [ CrossRef] [ PubMed] Muffly, L.; Liang, E.C.; Dolan, J.G.; Pulsipher, M.A. How I use next-generation sequencing–MRD to plan approach and prevent relapse after HCT for children and adults with ALL. Blood 2024, 144, 253–261. [] [ CrossRef] [ PubMed] Medina, A.; Puig, N.; Flores-Montero, J.; Jimenez, C.; Sarasquete, M.-E.; Garcia-Alvarez, M.; Prieto-Conde, I.; Chillon, C.; Alcoceba, M.; Gutierrez, N.C.; et al. Comparison of next-generation sequencing (NGS) and next-generation flow (NGF) for minimal residual disease (MRD) assessment in multiple myeloma. Blood Cancer J. 2020, 10, 108. [] [ CrossRef] Maffini, E.; Ursi, M.; Barbato, F.; Dicataldo, M.; Roberto, M.; Campanini, E.; Dan, E.; De Felice, F.; De Matteis, S.; Storci, G.; et al. The prevention of disease relapse after allogeneic hematopoietic cell transplantation in acute myeloid leukemia. Front. Oncol. 2022, 12, 1066285. [] [ CrossRef] Figure 1. PRISMA flow diagram: The systematic search of PubMed, Embase, Web of Science, Google Scholar, and EBSCO, along with manual screening, yielded 849 records. Following the removal of 241 duplicates, 608 records were screened by title/abstract. Full-text assessment of 48 articles against eligibility criteria led to the final inclusion of 10 observational cohort studies in the systematic review and meta-analysis. Figure 1. PRISMA flow diagram: The systematic search of PubMed, Embase, Web of Science, Google Scholar, and EBSCO, along with manual screening, yielded 849 records. Following the removal of 241 duplicates, 608 records were screened by title/abstract. Full-text assessment of 48 articles against eligibility criteria led to the final inclusion of 10 observational cohort studies in the systematic review and meta-analysis. Figure 3. Forest Plot for the Association Between ctDNA Positivity and Progression-Free Survival and Relapse. Forest plot of the pooled hazard ratio (HR) for the association between ctDNA positivity and Progression-Free Survival reported by all eligible studies [ 20, 21] ( A), and Forest plot of the pooled hazard ratio (HR) for the association between ctDNA positivity and Relapse [ 21, 25] ( B). In ( A, B), squares represent individual study HRs; a diamond represents the pooled HR from the random-effects meta-analysis. Horizontal lines denote 95% confidence intervals. The vertical dashed line indicates the null effect (HR = 1). I 2 quantifies statistical heterogeneity. Figure 3. Forest Plot for the Association Between ctDNA Positivity and Progression-Free Survival and Relapse. Forest plot of the pooled hazard ratio (HR) for the association between ctDNA positivity and Progression-Free Survival reported by all eligible studies [ 20, 21] ( A), and Forest plot of the pooled hazard ratio (HR) for the association between ctDNA positivity and Relapse [ 21, 25] ( B). In ( A, B), squares represent individual study HRs; a diamond represents the pooled HR from the random-effects meta-analysis. Horizontal lines denote 95% confidence intervals. The vertical dashed line indicates the null effect (HR = 1). I 2 quantifies statistical heterogeneity. Table 1. Main characteristics of the studies analyzed (Clinical aspects). Table 1. Main characteristics of the studies analyzed (Clinical aspects). Author Year Journal Country Study Design Patient -Count -Age -Gender Underlying Disease/HCT Procedure Main Findings Abbreviations: ctDNA: circulating tumour DNA, AML: Acute Myeloid Leukemia, ALL: Acute Lymphoblastic Leukemia, GVHD: graft-versus-host disease; NHL: Non-Hodgkin lymphoma; HL: Hodgkin lymphoma; DLBCL: Diffuse large B cells lymphoma; OS: overall survival, PFS: progression free survival, MM: Multiple myeloma, HCT: Hematopoietic cell transplantation, allo-HCT: Allogeneic hematopoietic cell transplantation, MDS: Myelodysplastic Syndrome, CLL: Chronic lymphocytic leukemia, MPN: Myeloproliferative Neoplasia, CMML: Chronic Myelomonocytic Leukemia, MRD: Minimal Residual Disease, TA-cfDNA: tumor-associated cell-free DNA, MFC: multiparameter flow cytometry, RQ-PCR: real-time quantitative polymerase chain reaction. Table 2. Summary of Survival Outcomes Associated with ctDNA/cfDNA Positivity. Table 2. Summary of Survival Outcomes Associated with ctDNA/cfDNA Positivity. Author (Year) HCT Type Disease Outcome Measured p-Value Key Findings Abbreviations: AML = acute myeloid leukemia, cfDNA-MRD = cell-free DNA minimal residual disease; TA-cfDNA = tumor-associated cell-free DNA, OS = overall survival, PFS = progression-free survival, RFS = relapse-free survival. Table 3. Summary of Studies Evaluating ctDNA/cfDNA for engraftment and Transplant-Related Mortality. Table 3. Summary of Studies Evaluating ctDNA/cfDNA for engraftment and Transplant-Related Mortality. Author (Year) Timing of Assessment Biomarker Outcome Association with ctDNA/cfDNA Notes Abbreviations: HCT: hematopoietic cell transplantation; NGS = new generation sequencing, NRM = non-relapse mortality, post-alloHCT: after allogeneic HCT. Table 4. Summary of Studies Reporting Association Between ctDNA/cfDNA and Relapse. Table 4. Summary of Studies Reporting Association Between ctDNA/cfDNA and Relapse. Author (Year) HCT Type Disease Sample Size ctDNA Assay Timing of Assessment Association with Relapse? Key Finding Abbreviations: HCT, hematopoietic cell transplantation; Allo-HCT, allogeneic hematopoietic cell transplantation; AML, acute myeloid leukemia; MDS, myelodysplastic syndrome; MM, multiple myeloma; ALL, acute lymphoblastic leukemia; NHL, non-Hodgkin lymphoma; HL, Hodgkin lymphoma; CLL, chronic lymphocytic leukemia; DLBCL, diffuse large B-cell lymphoma; MPN, myeloproliferative neoplasm; CMML, chronic myelomonocytic leukemia; TA-cfDNA, tumor-associated cell-free DNA; NGS, next-generation sequencing; ddPCR, droplet digital polymerase chain reaction; RQ-PCR, real-time quantitative polymerase chain reaction; cfDNA-MRD, cell-free DNA minimal residual disease; cfRNA, cell-free RNA; MFC, multiparameter flow cytometry; CIR, cumulative incidence of relapse. Table 5. Risk of Bias Assessment Using the QUIPS Tool. Table 5. Risk of Bias Assessment Using the QUIPS Tool. Study Study Participation Study Attrition Prognostic Factor Measurement Outcome Measurement Study Confounding Statistical Analysis & Reporting Overall Risk Table 6. Studies Included in Quantitative Meta-analysis and Characteristics of Pooled Effect Estimates. Table 6. Studies Included in Quantitative Meta-analysis and Characteristics of Pooled Effect Estimates. Study Biomarker Type Disease Outcome Included in Meta-analysis HR Source Adjusted Analysis Main Covariates Included Included in Quantitative Synthesis Notes Abbreviations: ctDNA = circulating tumor DNA; cfDNA = cell-free DNA; MRD = measurable residual disease; HR = hazard ratio; OS = overall survival; PFS = progression-free survival; RFS = relapse-free survival; AML = acute myeloid leukemia; MDS = myelodysplastic syndrome; MPN = myeloproliferative neoplasm; CMML = chronic myelomonocytic leukemia; DLBCL = diffuse large B-cell lymphoma; GVHD = graft-versus-host disease; KM = Kaplan–Meier. Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. MDPI and ACS Style Dac, D.T.; Tanaka, H.; Takami, A.; Espinoza, J.L. The Prognostic Value of Circulating Tumor DNA for Clinical Outcomes in Patients Undergoing Hematopoietic Cell Transplantation: A Systematic Review and Meta-Analysis. Int. J. Mol. Sci. 2026, 27, 5076. https://doi.org/10.3390/ijms27115076 AMA Style Dac DT, Tanaka H, Takami A, Espinoza JL. The Prognostic Value of Circulating Tumor DNA for Clinical Outcomes in Patients Undergoing Hematopoietic Cell Transplantation: A Systematic Review and Meta-Analysis. International Journal of Molecular Sciences. 2026; 27(11):5076. https://doi.org/10.3390/ijms27115076 Chicago/Turabian Style Dac, Do Tung, Hirokazu Tanaka, Akiyoshi Takami, and Jorge Luis Espinoza. 2026. "The Prognostic Value of Circulating Tumor DNA for Clinical Outcomes in Patients Undergoing Hematopoietic Cell Transplantation: A Systematic Review and Meta-Analysis" International Journal of Molecular Sciences 27, no. 11: 5076. https://doi.org/10.3390/ijms27115076 APA Style Dac, D. T., Tanaka, H., Takami, A., & Espinoza, J. L. (2026). The Prognostic Value of Circulating Tumor DNA for Clinical Outcomes in Patients Undergoing Hematopoietic Cell Transplantation: A Systematic Review and Meta-Analysis. International Journal of Molecular Sciences, 27(11), 5076. https://doi.org/10.3390/ijms27115076
